Upload interface2_v1.0.py

interface2_v1.0.py

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+import gradio as gr
+import pickle
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_community.chat_message_histories import SQLChatMessageHistory
+from langchain_groq import ChatGroq
+from langchain_core.runnables import RunnablePassthrough
+from langchain.schema.output_parser import StrOutputParser
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from operator import itemgetter
+
+# Load the vector store
+with open("qdrant_vectorstore.pkl", "rb") as f:
+    qdrant_vectorstore = pickle.load(f)
+
+# Updated function definition
+def echo_user_input(*args):
+    user_input = args[0]  # Extract user_input from args
+    
+    # Set up retriever
+    qdrant_retriever = qdrant_vectorstore.as_retriever()
+    found_docs = qdrant_vectorstore.similarity_search(user_input)
+
+    context_str = ""
+    for context_data in found_docs:
+        context_str += context_data.page_content + '\n\n'
+    
+    # Define prompt template
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", "Act as a helpful AI Assistant. Here is some {context}"),
+        MessagesPlaceholder(variable_name="history"),
+        ("human", "{human_input}")
+    ])
+
+    # Set up session history
+    def get_session_history(session_id):
+        return SQLChatMessageHistory(session_id, "sqlite:///memory.db")
+
+    # Initialize the LLM with Groq
+    groq_api_key = "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"
+    llm = ChatGroq(groq_api_key=groq_api_key, model_name="Gemma2-9b-It")
+
+    # Chain context with retriever
+    context = itemgetter("human_input") | qdrant_retriever
+    first_step = RunnablePassthrough.assign(context=context)
+    llm_chain = first_step | prompt | llm | StrOutputParser()
+
+    conv_chain = RunnableWithMessageHistory(llm_chain, get_session_history, input_messages_key="human_input", history_messages_key="history")
+
+    # Define a session ID for the conversation
+    session_id = 'bond007'
+    # return conv_chain.invoke(({"human_input": user_input}), {'configurable': {'session_id': session_id}})
+
+    llm_response = conv_chain.invoke(({"human_input": user_input}), {'configurable': {'session_id': session_id}})
+
+    # Combine context with the LLM response for Gradio output
+    combined_output = f"**Retrieved Context:**\n{context_str}\n\n**Response:**\n{llm_response}"
+    return combined_output
+
+# Define the Gradio chat interface
+interface = gr.ChatInterface(
+    fn=echo_user_input,
+    title="Comply2Reg Chat Assistant",
+    description="Type your question and press enter to see a conversational response. 🤖",
+)
+
+# Launch the interface with share=True for a public link
+if __name__ == "__main__":
+    interface.launch(share=True)